Methods and apparatus to determine media impressions using distributed demographic information

ABSTRACT

Examples to determine media impressions using distributed demographic information are disclosed. An example system includes programmable circuitry to log, at a first Internet domain, a first record for a first website visit by a first client device to a website at a second Internet domain, the first record based on a hypertext transfer protocol (HTTP) request, the first record to include a timestamp, a uniform resource locator (URL), and a user identifier, the timestamp to represent a time of the first website visit, the URL corresponding to the website at the second Internet domain, and weight impression data in a report, the impression data associated with the first record and with second records, the second records corresponding to second website visits to the website via second client devices, the weighting of the impression data based on demographic distributions of audience members corresponding to the first and second records.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser. No. 18/068,262, filed on Dec. 19, 2022, which is a continuation of U.S. patent application Ser. No. 17/199,139, filed on Mar. 11, 2021, now U.S. Pat. No. 11,533,379, which is a continuation of U.S. patent application Ser. No. 16/792,766, filed on Feb. 17, 2020, now U.S. Pat. No. 10,951,721, which is a continuation of U.S. patent application Ser. No. 16/364,961, filed on Mar. 26, 2019, now U.S. Pat. No. 10,567,531, which is a continuation of U.S. patent application Ser. No. 15/966,195, filed on Apr. 30, 2018, now U.S. Pat. No. 10,284,667, which is a continuation of U.S. patent application Ser. No. 15/409,281, filed on Jan. 18, 2017, now U.S. Pat. No. 9,979,614, which is a divisional of U.S. patent application Ser. No. 13/921,962, filed Jun. 19, 2013, now U.S. Pat. No. 9,596,150, which is a continuation of International Patent Application No. PCT/US11/65881, filed Dec. 19, 2011, which claims priority to U.S. Provisional Patent Application No. 61/424,952, filed on Dec. 20, 2010, all of which are hereby incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring media and, more particularly, to methods and apparatus to determine media impressions using distributed demographic information.

BACKGROUND

Traditionally, audience measurement entities determine audience engagement levels for media programming based on registered panel members. That is, an audience measurement entity enrolls people who consent to being monitored into a panel. The audience measurement entity then monitors those panel members to determine media programs (e.g., television programs or radio programs, movies, DVDs, etc.) exposed to those panel members. In this manner, the audience measurement entity can determine exposure measures for different media content based on the collected media measurement data.

Techniques for monitoring user access to Internet resources such as web pages, advertisements and/or other content has evolved significantly over the years. Some known systems perform such monitoring primarily through server logs. In particular, entities serving content on the Internet can use known techniques to log the number of requests received for their content at their server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example system to determine advertisement and/or content impressions using distributed demographic information.

FIG. 2 depicts an example manner of reporting cookies to an audience measurement entity and database proprietor(s) in response to users logging in to website(s) of the database proprietor(s).

FIG. 3 depicts an example manner in which a web browser can report impressions to an impression monitor of the example system of FIG. 1 .

FIG. 4 is an example apparatus that may be used to associate impressions with demographics of users registered with one or more database proprietors.

FIG. 5 is an example partner cookie map that may be used by an Internet service database proprietor to map user identifiers associated with an audience measurement entity with user identifiers of users registered with the Internet service database proprietor.

FIG. 6 is an example impressions table generated by the impression monitor system of the example system of FIG. 1 to correlate impressions with user identifiers of monitored audience members.

FIG. 7 depicts an example partner-based impressions table generated by an Internet service database proprietor to correlate impressions with user identifiers of registered users of the Internet service database proprietors.

FIG. 8 depicts an example impressions table showing quantities of impressions associated with monitored users.

FIG. 9 depicts an example campaign-level age/gender and impression composition table generated by a database proprietor.

FIG. 10 is a flow diagram representative of example machine readable instructions that may be executed to report login events and user cookies to database proprietors.

FIG. 11 is a flow diagram representative of example machine readable instructions that may be executed to map audience measurement entity (AME) cookie identifiers to user identifiers of users registered with a database proprietor.

FIG. 12 is a flow diagram representative of example machine readable instructions that may be executed to log impressions.

FIG. 13 is a flow diagram representative of example machine readable instructions that may be executed to generate demographics-based impressions reports.

FIG. 14 is an example processor system that can be used to execute the example instructions of FIGS. 10-13 to implement the example apparatus and systems of FIGS. 1, 2, 3 , and/or 4.

FIG. 15 is an example apparatus that may be used to implement the impression monitor of FIGS. 1-3 .

FIG. 16 is an example apparatus that may be used to implement a cookie reporter of FIG. 2 .

DETAILED DESCRIPTION

Techniques for monitoring user access to Internet resources such as web pages, advertisements and/or other content has evolved significantly over the years. At one point in the past, such monitoring was done primarily through server logs. In particular, entities serving content on the Internet would log the number of requests received for their content at their server. Basing Internet usage research on server logs is problematic for several reasons. For example, server logs can be tampered with either directly or via zombie programs that repeatedly request content from the server to increase the server log counts. Secondly, content is sometimes retrieved once, cached locally and then repeatedly viewed from the local cache without involving the server in the repeat viewings. Server logs cannot track these views of cached content. Thus, server logs are susceptible to both over-counting and under-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637, fundamentally changed the way Internet monitoring is performed and overcame the limitations of the server side log monitoring techniques described above. For example, Blumenau disclosed a technique wherein Internet content to be tracked is tagged with beacon instructions (e.g., tag instructions). In particular, monitoring instructions are associated with the HTML of the content (e.g., advertisements or other Internet content) to be tracked. When a client requests the content, both the content and the beacon or tag instructions are downloaded to the client either simultaneously (e.g., with the tag instructions present in the HTML) or via subsequent requests (e.g., via execution of a request to retrieve the monitoring instructions embedded in the HTML of the content). The tag instructions are, thus, executed whenever the content is accessed, be it from a server or from a cache.

The tag instructions cause monitoring data reflecting information about the access to the content to be sent from the client that downloaded the content to a monitoring entity. The monitoring entity may be an audience measurement entity that did not provide the content to the client and who is a trusted third party for providing accurate usage statistics (e.g., The Nielsen Company, LLC). Advantageously, because the tag instructions are associated with the content (e.g., embedded in or otherwise linked to some portion of the content) and executed by the client browser whenever the content is accessed, the monitoring information is provided to the audience measurement company irrespective of whether the client is a panelist of the audience measurement company.

In some instances, it is important to link demographics to the monitoring information. To address this issue, the audience measurement company establishes a panel of users who have agreed to provide their demographic information and to have their Internet browsing activities monitored. When an individual joins the panel, they provide detailed information concerning their identity and demographics (e.g., gender, race, income, home location, occupation, etc.) to the audience measurement company. The audience measurement entity sets a cookie (e.g., a panelist cookie) on the panelist computer that enables the audience measurement entity to identify the panelist whenever the panelist accesses tagged content (e.g., content associated with beacon or tag instructions) and, thus, sends monitoring information to the audience measurement entity.

Since most of the clients providing monitoring information from the tagged pages are not panelists and, thus, are unknown to the audience measurement entity, it has heretofore been necessary to use statistical methods to impute demographic information based on the data collected for panelists to the larger population of users providing data for the tagged content. However, panel sizes of audience measurement entities remain small compared to the general population of users. Thus, a problem is presented as to how to increase panel sizes while ensuring the demographics data of the panel is accurate.

There are many database proprietors operating on the Internet. These database proprietors provide services to large numbers of subscribers or registered users. In exchange for the provision of the service, the subscribers register with the proprietor. As part of this registration, the subscribers provide detailed demographic information. Examples of such database proprietors include social network providers such as Facebook, Myspace, etc. These database proprietors set cookies on the computing device (e.g., computer, cell phone, etc.) of their subscribers to enable the database proprietors to recognize the users when they visit their websites.

The protocols of the Internet make cookies inaccessible outside of the domain (e.g., Internet domain, domain name, etc.) on which they were set. Thus, a cookie set in the HFZlaw.com domain is accessible to servers in the HFZlaw.com domain, but not to servers outside that domain. Therefore, although an audience measurement entity might find it advantageous to access the cookies set by the database proprietors, they are unable to do so.

In view of the foregoing, it would be advantageous to leverage the existing databases of database proprietors to collect more extensive Internet usage and demographic data. However, there are several problems in accomplishing this end. For example, a problem is presented as to how to access the data of the database proprietors without compromising the privacy of the subscribers, the panelists, and/or the proprietors of the tracked content. Another problem is how to access this data given the technical restrictions imposed by the Internet protocols that prevent the audience measurement entity from accessing cookies set by the database proprietor. Example methods, apparatus and articles of manufacture disclosed herein solve these problems by extending the beaconing process to encompass partnered database proprietors and by using such partners as sources of distributed demographic information.

Example methods, apparatus, systems, and/or articles of manufacture disclosed herein cooperate with one or more database proprietors (also referred to herein as partners). The database proprietors provide Internet services to their registered users (e.g., users of those database proprietors) and store demographic information (e.g., in user account records) for those registered users. As part of this effort, the database proprietor agrees to provide demographic information of its registered users to the audience measurement entity for purposes of measuring demographic-based exposures to media such as content and/or advertisements. To prevent violating privacy agreements with the registered users of the database proprietor, examples disclosed herein employ cookie mapping techniques. That is, the database proprietor can maintain a mapping of its registered user cookies (i.e., partner cookies assigned by the database proprietor to its registered users) to cookies assigned by the audience measurement entity (i.e., audience measurement entity (AME) cookies) to the same registered users. In this manner, the audience measurement entity can log impressions of registered users based on the AME cookies and send full or partial AME cookie-based impression logs to a database proprietor. The database proprietor can, in turn, match its registered users to the AME cookie-based impressions based on its partner-to-AME cookie map. The database proprietor can then use the matches to associate demographic information for the matching registered users with corresponding impression logs. The database proprietor can then remove any identifying data (i.e., partner cookie data) from the demographic-based impression logs and provide the demographic-based impression logs to the audience measurement entity without revealing the identities of the database proprietor's registered users to the audience measurement entity. Thus, example techniques disclosed herein may be implemented without compromising privacies of registered users of database proprietors that partner with an audience measurement entity to track impressions based on audience demographics.

A database proprietor (e.g., Facebook) can access cookies it has set on a client device (e.g., a computer) to thereby identify the client based on the internal records (e.g., user account records) of the database proprietor. Because the identification of client devices is done with reference to enormous databases of registered users far beyond the quantity of persons present in a typical audience measurement panel, this process may be used to develop data that is extremely accurate, reliable, and detailed.

Because the audience measurement entity remains the first leg of the data collection process (i.e., receives tag requests generated by tag instructions from client devices to log impressions), the audience measurement entity is able to obscure the source of the content access being logged as well as the identity of the content (e.g., advertisements and/or other types of media) itself from the database proprietors (thereby protecting the privacy of the content sources), without compromising the ability of the database proprietors to provide demographic information corresponding to ones of their subscribers for which the audience measurement entity logged impressions.

Example methods, apparatus, and/or articles of manufacture disclosed herein can be used to determine impressions or exposures to advertisements and/or other types of media such as content using demographic information, which is distributed across different databases (e.g., different website owners, different service providers, etc.) on the Internet. Not only do example methods, apparatus, and articles of manufacture disclosed herein enable more accurate correlation of demographics to media impressions, but they also effectively extend panel sizes and compositions beyond persons participating (and/or willing to participate) in the panel of a ratings entity to persons registered in other Internet databases such as the databases of social media sites such as Facebook, Twitter, Google, etc. This extension effectively leverages the content tagging capabilities of the audience ratings entity and the use of databases of non-ratings entities such as social media and other websites to create an enormous, demographically accurate panel that results in accurate, reliable measurements of exposures to Internet content such as advertising and/or programming.

Traditionally, audience measurement entities (also referred to herein as “ratings entities”) determine demographic reach for advertising and media programming based on registered panel members. That is, an audience measurement entity enrolls people that consent to being monitored into a panel. During enrollment, the audience measurement entity receives demographic information from the enrolling people so that subsequent correlations may be made between advertisement/media exposure to those panelists and different demographic markets. Unlike traditional techniques in which audience measurement entities rely solely on their own panel member data to collect demographics-based audience measurements, example methods, apparatus, and/or articles of manufacture disclosed herein enable an audience measurement entity to obtain demographic information from other entities that operate based on user registration models. As used herein, a user registration model is a model in which users subscribe to services of those entities by creating user accounts and providing demographic-related information about themselves. Obtaining such demographic information associated with registered users of database proprietors enables an audience measurement entity to extend or supplement its panel data with substantially reliable demographics information from external sources (e.g., database proprietors), thus extending the coverage, accuracy, and/or completeness of their demographics-based audience measurements. Such access also enables the audience measurement entity to monitor persons who would not otherwise have joined an audience measurement panel.

Any entity having a database identifying demographics of a set of individuals may cooperate with the audience measurement entity. Such entities are referred to herein as “database proprietors” and include entities such as Facebook, Google, Yahoo!, MSN, Twitter, Apple iTunes, Experian, etc. Such database proprietors may be, for example, online web services providers. For example, a database proprietor may be a social network site (e.g., Facebook, Twitter, MySpace, etc.), a multi-service site (e.g., Yahoo!, Google, Experian, etc.), an online retailer site (e.g., Amazon.com, Buy.com, etc.), and/or any other web services site that maintains user registration records and irrespective of whether the site fits into none, one or more of the categories noted above.

Example methods, apparatus, and/or articles of manufacture disclosed herein may be implemented by an audience measurement entity, a ratings entity, or any other entity interested in measuring or tracking audience exposures to advertisements and/or any other media.

To increase the likelihood that measured viewership is accurately attributed to the correct demographics, example methods, apparatus, and/or articles of manufacture disclosed herein use demographic information located in the audience measurement entity's records as well as demographic information located at one or more database proprietors (e.g., web service providers) that maintain records or profiles of users having accounts therewith. In this manner, example methods, apparatus, and/or articles of manufacture may be used to supplement demographic information maintained by a ratings entity (e.g., an audience measurement company such as The Nielsen Company of Schaumburg, Illinois, United States of America, that collects media exposure measurements and/or demographics) with demographic information from one or more different database proprietors (e.g., web service providers).

The use of demographic information from disparate data sources (e.g., high-quality demographic information from the panels of an audience measurement company and/or registered user data of web service providers) results in improving the reporting effectiveness of metrics for online and/or offline advertising campaigns. Examples disclosed herein use online registration data to identify demographics of users. Such examples also use server impression counts, tagging (also referred to as beaconing), and/or other techniques to track quantities of advertisement and/or content impressions attributable to those users. Online web service providers such as social networking sites and multi-service providers (collectively and individually referred to herein as online database proprietors) maintain detailed demographic information (e.g., age, gender, geographic location, race, income level, education level, religion, etc.) collected via user registration processes. An impression corresponds to a home or individual having been exposed to the corresponding media content and/or advertisement. Thus, an impression represents a home or an individual having been exposed to an advertisement or content or group of advertisements or content. In Internet advertising, a quantity of impressions or impression count is the total number of times an advertisement or advertisement campaign has been accessed by a web population (e.g., including number of times accessed as decreased by, for example, pop-up blockers and/or increased by, for example, retrieval from local cache memory).

Example impression reports generated using example methods, apparatus, and/or articles of manufacture disclosed herein may be used to report TV GRPs and online GRPs in a side-by-side manner. For instance, advertisers may use impression reports to report quantities of unique people or users that are reached individually and/or collectively by TV and/or online advertisements.

Although examples are disclosed herein in connection with advertisements, advertisement exposures, and/or advertisement impressions, such examples may additionally or alternatively be implemented in connection with other types of media in addition to or instead of advertisements. That is, processes, apparatus, systems, operations, structures, data, and/or information disclosed herein in connection with advertisements may be similarly used and/or implemented for use with other types of media such as content. “Media” refers to content and/or advertisements. Websites, movies, television and/or other programming is generally referred to herein as content. Advertisements are typically distributed with content. Traditionally, content is provided at little or no cost to the audience because it is subsidized by advertisers who pay to have their advertisements distributed with the content.

Turning now to FIG. 1 , an example system 100 is shown. In the illustrated example, the system 100 includes an impression monitor system 102 which may be owned and/or operated by an audience measurement entity 103. In the illustrated examples, the impression monitor system 102 works cooperatively with one or more database proprietors, two of which are shown as a partner A database proprietor 104 a and a partner B database proprietor 104 b, to generate impression reports 106 a and 106 b using distributed demographic information collected by the database proprietors 104 a and 104 b. In the illustrated example, the impression reports 106 a and 106 b are indicative of demographic segments, populations, or groups that were exposed to identified advertisements or content. “Distributed demographics information” is used herein to refer to demographics information obtained from a database proprietor such as an online web services provider. In the illustrated example, the impression monitor system 102 may be owned and/or operated by an audience measurement entity to collect and log impressions from client devices 108 using, for example, audience measurement entity (AME) cookies set on those client devices 108. In illustrated examples described herein, AME cookies (e.g., an AME cookie 208 of FIG. 2 ) are set in the client devices 108 in response to contacting the audience measurement entity 103 after executing monitoring or tag instructions regardless of whether all, some, or none of the client devices 108 are associated with audience member panels of the audience measurement entity 103. That is, by setting AME cookies in the client devices 108, the audience measurement entity 103 is able to log ad and/or content impressions regardless of whether the ad and/or content impressions are attributable to panelists or non-panelists. In the illustrated example of FIG. 1 , the client devices 108 may be stationary or portable computers, handheld computing devices, smart phones, Internet appliances, and/or any other type of device that may be connected to the Internet and capable of presenting media content.

In the illustrated example, content providers and/or advertisers distribute advertisements 110 via the Internet to users that access websites and/or online television services (e.g., web-based TV, Internet protocol TV (IPTV), etc.). In the illustrated example, the advertisements 110 may be individual, stand alone ads and/or may be part of one or more ad campaigns. The ads of the illustrated example are encoded with identification codes (i.e., data) that identify the associated ad campaign (e.g., campaign ID, if any), a creative type ID (e.g., identifying a Flash-based ad, a banner ad, a rich type ad, etc.), a source ID (e.g., identifying the ad publisher), and/or a placement ID (e.g., identifying the physical placement of the ad on a screen). The advertisements 110 of the illustrated example are also tagged or encoded to include computer executable monitoring instructions (e.g., Java, java script, or any other computer language or script) that are executed by web browsers that access the advertisements 110 via, for example, the Internet. In the illustrated example of FIG. 1 , the advertisements 110 are presented to audience members via the client devices 108. Computer executable monitoring instructions may additionally or alternatively be associated with content to be monitored. Thus, although this disclosure frequently speaks in terms of tracking advertisements, it is not restricted to tracking any particular type of media. On the contrary, it can be used to track media (e.g., content and/or advertisements) of any type or form in a network. Irrespective of the type of media being tracked, execution of the monitoring instructions causes the web browser to send impression requests 112 (e.g., referred to herein as tag requests 112) to a specified server (e.g., the audience measurement entity). The tag requests 112 may be implemented using HTTP requests. However, whereas HTTP requests traditionally identify web pages or other resources to be downloaded, the tag requests 112 of the illustrated example include audience measurement information (e.g., ad campaign identification, content identifier, and/or user identification information) as their payloads. The server (e.g., the impression monitor system 102) to which the tag requests 112 are directed is programmed to log the audience measurement data caused by the tag requests 112 as impressions (e.g., ad and/or content impressions depending on the nature of the media tagged with the monitoring instructions). To collect and log exposure measurements, the impression monitor system 102 includes an AME impressions store 114. Example impression logging processes are described in detail below in connection with FIG. 3 .

In some examples, advertisements tagged with such tag instructions are distributed with Internet-based media content such as, for example, web pages, streaming video, streaming audio, IPTV content, etc. As noted above, methods, apparatus, systems, and/or articles of manufacture disclosed herein are not limited to advertisement monitoring but can be adapted to any type of content monitoring (e.g., web pages, movies, television programs, etc.) Example techniques that may be used to implement such monitoring, tag and/or beacon instructions are described in Blumenau, U.S. Pat. No. 6,108,637, which is hereby incorporated herein by reference in its entirety.

In the illustrated example of FIG. 1 , the impression monitor system 102 tracks users associated with impressions using AME cookies (e.g., name-value pairs of Universally Unique Identifiers (UUIDs)) when the client devices 108 present advertisements (e.g., the advertisements 110) and/or other content. Due to Internet security protocols, the impression monitor system 102 can only collect cookies set in its domain (e.g., AME cookies). Thus, if, for example, the impression monitor system 102 operates in the “Nielsen.com” domain, it can only collect cookies set in the Nielsen.com domain. Thus, when the impression monitor system 102 receives tag requests 112 from the client devices 108, the impression monitor system 102 only has access to AME cookies set on that client device for, for example, the Nielsen.com domain, but not cookies set outside its domain (e.g., outside the Nielsen.com domain).

To overcome the domain limitations associated with collecting cookie information, the impression monitoring system 102 monitors impressions of users of the client devices 108 that are registered users of one or both of the partner A and partner B database proprietors 104 a and 104 b. When a user of one of the client devices 108 logs into a service of one of the database proprietors 104 a or 104 b, the client device 108 performs an initialization (INIT) AME cookie message exchange 116 with the impression monitor system 102 and sends a login reporting message 118 to the database proprietor providing that service. For example, as described in more detail below in connection with FIG. 2 , if a user logs into a service of the partner A database proprietor 104 a, the INIT AME cookie message exchange 116 sets an AME cookie in the client device 108 based on the domain of the impression monitor system 102 for the user that logged into the service of the partner A database proprietor 104 a. In addition, the login reporting message 118 sent to the partner A database proprietor 104 a includes the same AME cookie for the client device 108 and a partner A cookie set by the partner A database proprietor 104 a for the same client device 108. In the illustrated example, the partner A database proprietor 104 a sets the partner A cookie in the client device 108 when the client device 108 visits a webpage of the partner A database proprietor 104 a and/or when a user logs into a service of the partner A database proprietor 104 a via a login page of the partner A database proprietor 104 a (e.g., the login webpage 204 of FIG. 2 ). In the illustrated example, the AME cookie is outside a domain (e.g., a root domain) of the partner A cookie. The login reporting message 118 enables the partner A database proprietor 104 a to map its partner A cookie to the AME cookie for the user of the client device 108. The INIT AME cookie message exchange 116 includes a login timestamp indicative of when a user associated with the specified AME cookie logged into the partner A database proprietor 104 a. If an AME cookie was previously set for the client, a new AME cookie is not set unless the previous AME cookie has been removed from the client, is not longer present on the client, and/or has expired. These processes are described in greater detail below in connection with FIG. 2 .

Subsequently, the impression monitor system 102 receives the tag requests 112 based on ads and/or content presented via the client devices 108 and logs impressions based on the presented ads and/or content in association with respective AME cookies of the client devices 108 as described in detail below in connection with FIG. 3 . In the illustrated example of FIG. 1 , the impression monitor system 102 stores the logged impressions in the AME impressions store 114 and subsequently sends AME impression logs 122 containing some or all of the logged impressions from the AME impressions store 114 to the partner database proprietors 104 a and 104 b.

Each of the partner database proprietors 104 a-b may subsequently use their respective AME cookie-to-partner cookie mappings to match demographics of users of the client devices 108 identified based on partner cookies with impressions logged based on AME cookies in the AME impression logs 122. Example demographic matching and reporting is described in greater detail below in connection with FIG. 4 . Because the audience measurement entity 103 sets AME cookies on any client that sends it a tag request (i.e., including non-panelists), the map of the AME cookies to partner cookies is not limited to panelists but instead extends to any client that accesses tagged media. As a result, the audience measurement entity 103 is able to leverage the data of the partner as if the non-panelists with AME cookies were panelists of the audience measurement entity 103, thereby effectively increasing the panel size. In some examples, the panel of the audience measurement entity is eliminated.

FIG. 2 depicts an example manner of setting cookies with the impression monitor system 102 and reporting the same to the database proprietors (e.g., the partner A database proprietor 104 a and/or the partner B database proprietor 104 b) in response to users logging in to websites of the database proprietors. One of the client devices 108 of FIG. 1 is shown in FIG. 2 and is provided with a cookie reporter 202 configured to monitor login events on the client device 108 and to send cookies to the impression monitor system 102 and the database proprietors 104 a and 104 b. In the illustrated example of FIG. 2 , the cookie reporter 202 is shown performing the INIT AME cookie message exchange 116 with the impression monitor system 102 and sending the login reporting message 118 to the partner A database proprietor 104 a.

In the illustrated example of FIG. 2 , the cookie reporter 202 is implemented using computer executable instructions (e.g., Java, java script, or any other computer language or script) that are executed by web browsers. Also in the illustrated example of FIG. 2 , the cookie reporter 202 is provided to the clients, directly or indirectly, by an audience measurement entity that owns and/or operates the impression monitor system 102. For example, the cookie reporter 202 may be provided to the database proprietor from the AME 103 and subsequently downloaded to the client device 108 from a server serving a login webpage 204 of the partner A database proprietor 104 a (or of the partner B database proprietor 104 b or of any other partner database proprietor) in response to the client device 108 requesting the login webpage.

A web browser of the client device 108 may execute the cookie reporter 202 to monitor for login events associated with the login page 204. When a user logs in to a service of the partner A database proprietor 104 a via the login page 204, the cookie reporter 202 initiates the INIT AME message exchange 116 by sending a request 206 to the impression monitor system 102. In the illustrated example of FIG. 2 , the request 206 is a dummy request because its purpose is not to actually retrieve a webpage, but is instead to cause the impression monitor system 102 to generate an AME cookie 208 for the client device 108 (assuming an AME cookie has not already been set for and/or is not present on the client). The AME cookie 208 uniquely identifies the client device 108. However, because the client device 108 may not be associated with a panelist of the AME 103, the identity and/or characteristics of the user may not be known. The impression monitor system 102 subsequently uses the AME cookie 208 to track or log impressions associated with the client device 108, irrespective of whether the client device 108 is a panelist of the AME 103, as described below in connection with FIG. 3 . Because disclosed examples monitor clients as panelists even though they may not have been registered (i.e., have not agreed to be a panelist of the AME 103), such clients may be referred to herein as pseudo-panelists.

The request 206 of the illustrated example is implemented using an HTTP request that includes a header field 210, a cookie field 212, and a payload field 214. The header field 210 stores standard protocol information associated with HTTP requests. When the client device 108 does not yet have an AME cookie set therein, the cookie field 212 is empty to indicate to the impression monitor system 102 that it needs to create and set the AME cookie 208 in the client device 108. In response to receiving a request 206 that does not contain an AME cookie 208, the impression monitor system 102 generates an AME cookie 208 and sends the AME cookie 208 to the client device 108 in a cookie field 218 of a response message 216 as part of the INIT AME cookie message exchange 116 of FIG. 1 to thereby set the AME cookie 208 in the client device 108.

In the illustrated example of FIG. 2 , the impression monitor system 102 also generates a login timestamp 220 indicative of a time at which a user logged in to the login page 204 and sends the login timestamp 220 to the client device 208 in a payload field 222 of the response 216. In the illustrated example, the login timestamp 220 is generated by the impression monitor system 102 (e.g., rather than the client device 108) so that all login events from all client devices 108 are time stamped based on the same clock (e.g., a clock of the impression monitor system 102). In this manner, login times are not skewed or offset based on clocks of respective client devices 108, which may have differences in time between one another. In some examples, the timestamp 220 may be omitted from the payload 222 of the response 216, and the impression monitor system 102 may instead indicate a login time based on a timestamp in a header field 224 of the response 216. In some examples, the response 216 is an HTTP 302 redirect response which includes a URL 226 of the partner A database proprietor 104 a to which the cookie reporter 202 should send the AME cookie 208. The impression monitor system 102 populates the redirect response with the URL.

In the illustrated example of FIG. 2 , after receiving the response 216, the cookie reporter 202 generates and sends the login reporting message 118 to the partner A database proprietor 104 a. For example, the cookie reporter 202 of the illustrated example sends the login reporting message 118 to a URL indicated by the login page 204. Alternatively, if the response 216 is an HTTP 302 redirect and includes the URL 226, the cookie reporter 202 sends the login reporting message 118 to the partner A database proprietor 104 a using the URL 226. In the illustrated example of FIG. 2 , the login reporting message 118 includes a partner A cookie 228 in a cookie field 230. The partner A cookie 228 uniquely identifies the client device 108 for the partner A database proprietor 104 a. Also in the illustrated example, the cookie reporter 202 sends the AME cookie 208 and the login timestamp 220 in a payload field 232 of the login reporting message 118. Thus, in the illustrated example of FIG. 2 , the AME cookie 208 is sent as regular data (e.g., a data parameter) or payload in the login reporting message 118 to the partner A database proprietor 104 a to overcome the fact that the AME cookie 208 was not set in the domain of the partner A database proprietor 104 a. In the illustrated example, the AME cookie 208 corresponds to another domain (e.g., a Nielsen.com root domain) outside the domain of the partner A cookie 228 (e.g., a Facebook.com root domain). Using example processes illustrated in FIG. 2 advantageously enables sending cookie data across different domains, which would otherwise not be possible using known cookie communication techniques. The database proprietor 104 a receives the AME cookie 208 in association with the partner A cookie 228, thereby, creating an entry in an AME cookie-to-partner cookie map (e.g., the partner cookie map 236).

Although the login reporting message 118 is shown in the example of FIG. 2 as including the partner A cookie 228, for instances in which the partner A database proprietor 104 a has not yet set the partner A cookie 228 in the client device 108, the cookie field 230 is empty in the login reporting message 118. In this manner, the empty cookie field 230 prompts the partner A database proprietor 104 a to set the partner A cookie 228 in the client device 108. In such instances, the partner A database proprietor 104 a sends the client device 108 a response message (not shown) including the partner A cookie 228 and records the partner A cookie 228 in association with the AME cookie 208.

In some examples, the partner A database proprietor 104 a uses the partner A cookie 228 to track online activity of its registered users. For example, the partner A database proprietor 104 a may track user visits to web pages hosted by the partner A database proprietor 104 a, display those web pages according to the preferences of the users, etc. The partner A cookie 228 may also be used to collect “domain-specific” user activity. As used herein, “domain-specific” user activity is user Internet activity associated within the domain(s) of a single entity. Domain-specific user activity may also be referred to as “intra-domain activity.” In some examples, the partner A database proprietor 104 a collects intra-domain activity such as the number of web pages (e.g., web pages of the social network domain such as other social network member pages or other intra-domain pages) visited by each registered user and/or the types of devices such as mobile devices (e.g., smart phones) or stationary devices (e.g., desktop computers) used for access. The partner A database proprietor 104 a may also track account characteristics such as the quantity of social connections (e.g., friends) maintained by each registered user, the quantity of pictures posted by each registered user, the quantity of messages sent or received by each registered user, and/or any other characteristic of user accounts.

In some examples, the cookie reporter 202 is configured to send the request 206 to the impression monitor system 102 and send the login reporting message 118 to the partner A database proprietor 104 a only after the partner A database proprietor 104 a has indicated that a user login via the login page 204 was successful. In this manner, the request 206 and the login reporting message 118 are not performed unnecessarily should a login be unsuccessful. In the illustrated example of FIG. 2 , a successful login ensures that the partner A database proprietor 104 a will associate the correct demographics of a logged in registered user with the partner A cookie 228 and the AME cookie 208.

In the illustrated example of FIG. 2 , the partner A database proprietor 104 a includes a server 234, a partner cookie map 236, and a user accounts database 238. Although not shown, other database proprietors (e.g., the partner B database proprietor 104 b of FIG. 1 ) that partner with the audience measurement entity 103 (FIG. 1 ) also include a respective partner cookie map similar to the partner cookie map 236 and a user accounts database similar to the user accounts database 238 but, of course, relative to their own users. The server 234 of the illustrated example communicates with the client device 108 to, for example, receive login information, receive cookies from the client device 108, set cookies in the client device 108, etc.

The partner cookie map 236 stores partner cookies (e.g., the partner A cookie 228) in association with respective AME cookies (e.g., the AME cookie 208) and respective timestamps (e.g., the timestamp 220). In the illustrated example of FIG. 2 , the partner cookie map 236 stores a unique user ID (UUID) found in a name-value pair (i.e., a parameter name such as ‘user ID’ and a value such as the UUID) of the partner A cookie 228 in association with a unique user ID found in a name-value pair of the AME cookie 208. In addition, the partner cookie map 236 stores the login timestamp 220 in association with the UUIDs to indicate a time at which a corresponding user login occurred. Referring briefly to FIG. 5 , an example implementation of the partner cookie map 236 is shown, in which an AME user ID column 502 stores UUIDs from AME cookies (e.g., the AME cookie 208 of FIG. 2 ), a partner user ID column 504 stores UUIDs from partner cookies (e.g., the partner A cookie 228 of FIG. 2 ), and a login timestamp column 506 stores login timestamps (e.g., the login timestamp 220 of FIG. 2 ). In illustrated examples disclosed herein, the partner A database proprietor 104 a uses the partner cookie map 236 to match impressions received from the impression monitor system 102 based on AME cookies (e.g., the AME cookie 208) to registered users of the partner A database proprietor 104 a identified by respective partner A cookies (e.g., the partner A cookie 228). In this manner, the partner A database proprietor 104 a can determine which of its registered users are associated with specific impressions logged by the impression monitor system 102.

Returning to FIG. 2 , the partner A database proprietor 104 a uses the user accounts database 238 to store, among other things, demographic information for registered users of the partner A database proprietor 104 a. In the illustrated example of FIG. 2 , such demographic information is received from registered users during an enrollment and/or registration process or during a subsequent personal information update process. The demographic information stored in the user accounts database 238 may include, for example, age, gender, interests (e.g., music interests, movie interests, product interests, or interests associated with any other topic), number of friends or social connections maintained by each registered user via the partner A database proprietor 104 a, personal yearly income, household income, geographic location of residence, geographic location of work, graduation year(s), quantity of group associations, or any other demographic information. The partner A database proprietor 104 a uses the user accounts database 238 to associate demographic information to particular impressions logged by the impression monitor system 102 after determining which registered users of the partner A database proprietor 104 a correspond to which logged impressions based on the partner cookie map 236.

FIG. 3 depicts an example system 300 that may be used to log impressions at the impression monitor system 102 of the example system 100 of FIG. 1 . The example system 300 enables the impressions monitor system 102 of FIGS. 1 and 2 to log impressions in association with corresponding AME cookies (e.g., the AME cookie 208 of FIG. 2 ) based on tag requests (e.g., the tag requests 112 of FIG. 1 ) received from a web browser 302 executed by a client device (e.g., any client device 108 of FIGS. 1 and 2 ). In the illustrated example of FIG. 3 , the impression monitor system 102 logs impressions from any client device (e.g., the client devices 108 of FIG. 1 ) from which it receives a tag request 112 as described below. The impression monitor system 102 compiles the received impression data in the AME impression data store 114.

Turning in detail to FIG. 3 , the client device may be any one of the client devices 108 of FIGS. 1 and 2 or another device not shown in FIG. 1 or 2. However, for simplicity of discussion and without loss of generality, the client device will be referred to as client device 108. As shown, the client device 108 sends communications to the impressions monitor system 102. In the illustrated example, the client device 108 executes the web browser 302, which is directed to a host website (e.g., www.acme.com) that displays one of the advertisement(s) 110 received from an ad publisher 303. The advertisement 110 of the illustrated example is tagged with identifier information (e.g., a campaign ID, a creative type ID, a placement ID, a publisher source URL, etc.) and tag instructions 304. When the tag instructions 304 are executed by the client device 108, the tag instructions 304 cause the client device 108 to send a tag request 112 to a URL address of the impressions monitor system 102 as specified in the tag instructions 304. Alternatively, the URL address specified in the tag instructions 304 may direct the tag request 112 to any other server owned, operated, and/or accessible by the audience measurement entity 103 (FIG. 1 ) or another entity. The tag instructions 304 may be implemented using java script or any other type(s) of executable instruction(s) including, for example, Java, HTML, etc. It should be noted that tagged content such as web pages, and/or any other media are processed the same way as the tagged advertisement 110. That is, for any tagged media, corresponding tag instructions are received in connection with the download of the tagged content and cause a tag request to be sent from the client device that downloaded the tagged content to the impression monitor system 102 (or any other server indicated by the instructions).

In the illustrated example of FIG. 3 , the tag request 112 is implemented using an HTTP request and is shown in detail as including a header field 310, a cookie field 312, and a payload field 314. In the illustrated example of FIG. 3 , the web browser 302 stores the AME cookie 208 of FIG. 2 in the cookie field 312 and stores ad campaign information 316 and a publisher site ID 318 in the payload field 314. In the illustrated example, the ad campaign information 316 may include information identifying one or more of an associated ad campaign (e.g., an ad campaign ID), a creative type ID (e.g., identifying a Flash-based ad, a banner ad, a rich type ad, etc.), and/or a placement ID (e.g., identifying the physical placement of the ad on a screen). In some examples, to log a content impression, the ad campaign information 316 is replaced with content information identifying the content (e.g., a content identifier), a creative ID, and/or a placement ID. In the illustrated example, the publisher site ID 318 identifies a source of the advertisement 110 and/or content (e.g., a source ID identifying the ad publisher 303 and/or content publisher).

In the illustrated example, in response to receiving the tag request 112, the impression monitor system 102 logs an impression associated with the client device 108 in the AME impressions store 114 by storing the AME cookie 208 in association with a content identifier (e.g., the ad campaign information 316 and/or the publisher site ID 318). In addition, the impression monitor system 102 generates a timestamp indicative of the time/date of when the impression occurred and stores the timestamp in association with the logged impression. An example implementation of the example AME impression store 114 is shown in FIG. 6 . Turning briefly to FIG. 6 , the AME impression store 114 includes an AME user ID column 602 to store AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3 ), a timestamp column 604 to store impression timestamps indicative of when impressions occurred at client devices (e.g., the client device 108 of FIGS. 1-3 ), a campaign ID column 606 to store the campaign information 316 of FIG. 3 , and a site ID column 608 to store the publisher site ID 318 of FIG. 3 .

FIG. 4 is an example apparatus 400 that may be used to associate impressions with demographics of users (e.g., users of the client devices 108 of FIGS. 1-3 ) registered with one or more database proprietors (e.g., the partner database proprietors 104 a-b of FIGS. 1-3 ). In some examples, the apparatus 400 is implemented at one or more database proprietors (e.g., the partner database proprietors 104 a-b of FIGS. 1-3 ). Alternatively, the apparatus 400 may be implemented at other sites. In some examples, the apparatus 400 may be developed by the audience measurement entity 103 (FIG. 1 ) and provided to a database proprietor to enable the database proprietor to combine database proprietor-owned demographic information with impression logs provided by the audience measurement entity 103. To ensure privacy of registered users of a database proprietor, the audience measurement entity 103 may install or locate the example apparatus 400 at a database proprietor so that the database proprietor need not provide identities of its registered users to the audience measurement entity 103 in order to associate demographics information with logged impressions. Instead, the audience measurement entity 103 can provide its logged impressions (e.g., the AME impression logs 122) to the database proprietor and the database proprietor can associate respective demographics with the logged impressions while concealing the identities (e.g., names and content information) of its registered users.

In the illustrated example, the apparatus 400 is provided with an example cookie matcher 402, an example demographics associator 404, an example demographics analyzer 406, an example demographics modifier 408, an example user ID modifier 410, an example report generator 412, an example data parser 414, an example mapper 416, and an example instructions interface 418. While an example manner of implementing the apparatus 400 has been illustrated in FIG. 4 , one or more of the elements, processes and/or devices illustrated in FIG. 4 may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the cookie matcher 402, the demographics associator 404, the demographics analyzer 406, the demographics modifier 408, the user ID modifier 410, the report generator 412, the data parser 414, the mapper 416, the instructions interface 418 and/or, more generally, the example apparatus 400 of FIG. 4 may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the cookie matcher 402, the demographics associator 404, the demographics analyzer 406, the demographics modifier 408, the user ID modifier 410, the report generator 412, the data parser 414, the mapper 416, the instructions interface 418 and/or, more generally, the example apparatus 400 could be implemented by one or more circuit(s), programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of the apparatus or system claims of this patent are read to cover a purely software and/or firmware implementation, at least one of the cookie matcher 402, the demographics associator 404, the demographics analyzer 406, the demographics modifier 408, the user ID modifier 410, the report generator 412, the data parser 414, the mapper 416, and/or the instructions interface 418 are hereby expressly defined to include a tangible computer readable medium such as a memory, DVD, CD, BluRay disk, etc. storing the software and/or firmware. Further still, the example apparatus 400 of FIG. 4 may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in FIG. 4 , and/or may include more than one of any or all of the illustrated elements, processes and devices.

Turning in detail to FIG. 4 , in the illustrated example, the apparatus 400 is implemented at the partner A database proprietor 104 a (FIGS. 1 and 2 ). Other instances of the apparatus 400 could be similarly implemented at any other database proprietor participating with the AME 103 (e.g., the partner B database proprietor 104 b). In the illustrated example of FIG. 4 , the apparatus 400 receives the AME impression logs 122 from the impression monitor system 102 to enable the apparatus 400 to associate user/audience member demographics from the user accounts database 238 with logged impressions.

In the illustrated example, the apparatus 400 is provided with the cookie matcher 402 to match AME user IDs from AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3 ) from the AME impression logs 122 to AME user IDs in the partner A cookie map 236. The apparatus 400 performs such cookie matching to identify registered users of the partner A database proprietor 104 a to which the logged impressions are attributable (e.g., partner A registered users for which the impression monitor system 102 set AME cookies as described above in connection with FIG. 2 and tracked impressions as described above in connection with FIG. 3 ). For example, the partner cookie map 236 is shown in FIG. 5 as associating AME user IDs in the AME user ID column 502 to partner user IDs in the partner user ID column 504. The AME impression logs 122 are structured similar to the data in the AME impression store 114 as shown in FIG. 6 , which logs impressions per AME user ID. Thus, the cookie matcher 402 matches AME user IDs from the AME user ID column 602 of the AME impression logs 122 to AME user IDs of the AME user ID column 502 of the partner cookie map 236 to associate a logged impression from the AME impression logs 122 to a corresponding partner user ID mapped in the partner cookie map 236 of FIG. 5 . In some examples, the AME 103 encrypts, obfuscates, varies, etc. campaign IDs in the AME impression logs 122 before sending the AME impression logs 122 to partner database proprietors (e.g., the partner database proprietors 104 a and 104 b of FIGS. 1 and 2 ) to prevent the partner database proprietors from recognizing the content to which the campaign IDs correspond or to otherwise protect the identity of the content. In such examples, a lookup table of campaign ID information may be stored at the impression monitor system 102 so that impression reports (e.g., the impression reports 106 a and 106 b of FIG. 1 ) received from the partner database proprietors can be correlated with the content.

In some examples, the cookie matcher 402 uses login timestamps (e.g., the login timestamp 220 of FIG. 2 ) stored in the login timestamp column 506 of FIG. 5 and impression timestamps stored in the timestamp column 604 of FIG. 6 to discern between different users to which impressions logged by the impression monitor system 102 are attributable. That is, if two users having respective username/password login credentials for the partner A database proprietor 104 a use the same client device 108, all impressions logged by the impression monitor system 102 will be based on the same AME cookie (e.g., the AME cookie 208 of FIGS. 2 and 3 ) set in the client device 108 regardless of which user was using the client device 108 when the impression occurred. However, by comparing logged impression timestamps (e.g., in the timestamp column 604 of FIG. 6 ) to login timestamps (e.g., in the login timestamp column 506 of FIG. 5 ), the cookie matcher 402 can determine which user was logged into the partner A database proprietor 104 a when a corresponding impression occurred. For example, if a user TOM′ logged in to the partner A database proprietor 104 a at 12:57 AM on Jan. 1, 2010 and a user ‘MARY’ logged in to the partner A database proprietor 104 a at 3:00 PM on Jan. 1, 2010 using the same client device 108, the login events are associated with the same AME cookie (e.g., the AME cookie 208 of FIGS. 2 and 3 ). In such an example, the cookie matcher 402 associates any impressions logged by the impression monitor system 102 for the same AME cookie between 12:57 AM and 3:00 pm on Jan. 1, 2010 to the user TOM′ and associates any impressions logged by the impression monitor system 102 for the same AME cookie after 3:00 pm on Jan. 1, 2010 to the user ‘MARY’. Such time-based associations are shown in the illustrated example data structure of FIG. 7 described below.

In the illustrated example, the cookie matcher 402 compiles the matched results into an example partner-based impressions data structure 700, which is shown in detail in FIG. 7 . Turning briefly to FIG. 7 , the partner-based impressions structure 700 includes an AME user ID column 702, an impression timestamp column 704, a campaign ID column 706, a site ID column 708, a user login timestamp 710, and a partner user ID column 712. In the AME user ID column 702, the cookie matcher 402 stores AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3 ). In the impression timestamp column 704, the cookie matcher 402 stores timestamps generated by the impression monitor system 102 indicative of when each impression was logged. In the campaign ID column 706, the cookie matcher 402 stores ad campaign IDs stored in, for example, the campaign information 316 of FIG. 3 . In some examples, instead of or in addition to the campaign ID column 706, the partner-based impressions data structure 700 includes a content ID column to store identifying information of content. In some examples, some content (e.g., advertisements and/or other media) is not associated with a campaign ID or content ID. In the illustrated example of FIG. 7 , blanks in the campaign ID column 706 indicate content that is not associated with campaign IDs and/or content IDs. In the site ID column 708, the cookie matcher 402 stores advertisement publisher site IDs (e.g., the publisher site ID 318 of FIG. 3 ). In the user login timestamp column 710, the cookie matcher 402 stores timestamps (e.g., the timestamp 220 of FIG. 2 ) indicative of when respective users logged in via partner login pages (e.g., the login page 204 of FIG. 2 ). In the partner user ID column 712, the cookie matcher 402 stores partner cookies (e.g., the partner A cookie 228 of FIG. 2 ).

Returning to FIG. 4 , in the illustrated example, the apparatus 400 is provided with the demographics associator 404 to associate demographics information from the user accounts database 238 with corresponding partner-based impressions from the partner-based impressions structure 700. For example, the demographics associator 404 may retrieve demographics information for partner user IDs noted in the partner user ID column 712 (FIG. 7) and associate the retrieved demographics information with corresponding ones of the records in the partner-based impressions structure 700.

In the illustrated example of FIG. 4 , to analyze demographic information for accuracy and/or completeness, the apparatus 400 is provided with the demographics analyzer 406. In addition, to update, modify, and/or fill-in demographics information in inaccurate and/or incomplete records, the apparatus 400 is provided with the demographics modifier 408. In some examples, the demographics analyzer 406 and/or the demographics modifier 408 analyze and/or adjust inaccurate demographic information using example methods, systems, apparatus, and/or articles of manufacture disclosed in U.S. patent application Ser. No. 13/209,292, filed on Aug. 12, 2011, and titled “Methods and Apparatus to Analyze and Adjust Demographic Information,” which is hereby incorporated herein by reference in its entirety.

In the illustrated example, to remove user IDs from the partner-based impressions structure 700 after adding the demographics information and before providing the data to the AME 103, the apparatus 400 of the illustrated example is provided with a user ID modifier 410. In the illustrated example, the user ID modifier 410 is configured to at least remove partner user IDs (from the partner user ID column 712) to protect the privacy of registered users of the partner A database proprietor 104 a. In some examples, the user ID modifier 410 may also remove the AME user IDs (e.g., from the AME user ID column 702) so that the impression reports 106 a generated by the apparatus 400 are demographic-level impression reports. “Removal” of user IDs (e.g., by the user ID modifier 410 and/or by the report generator 412) may be done by not providing a copy of the data in the corresponding user ID fields as opposed to deleting any data from those fields. If the AME user IDs are preserved in the impressions data structure 700, the apparatus 400 of the illustrated example can generate user-level impression reports.

In the illustrated example of FIG. 4 , to generate the impression reports 106 a, the apparatus 400 is provided with the report generator 412. Example information that the report generator 412 may generate for the impression reports 106 a is described in detail below in connection with FIGS. 8 and 9 .

In the illustrated example of FIG. 4 , to parse information, the apparatus 400 is provided with the data parser 414. In some examples, the data parser 414 receives messages from client devices and/or other systems and parses information from those received messages. For example, the apparatus 400 may use the data parser 414 to receive the login reporting message 118 from the cookie reporter 202 (FIG. 2 ) and parse out the partner A cookie 228, the AME cookie 208, and/or the login timestamp 220 from the login reporting message 118. In some examples, the apparatus 400 also uses the data parser 414 to parse information in the AME impression logs 122 and/or to parse information from any other data structure and/or message.

In the illustrated example of FIG. 4 , to map information, the apparatus 400 is provided with the mapper 416. In some examples, the mapper 416 maps cookie identifiers associated with the same user but corresponding to different Internet domains. For example, the apparatus 400 may use the mapper 416 to map the partner A cookie 228 to the AME cookie 208 (FIG. 2 ) in the partner cookie map 236 (FIGS. 2, 4, and 5 ). In some examples, the mapper 416 also maps login timestamps with corresponding cookie identifiers. For example, the apparatus 400 may use the mapper 416 to map the login timestamp 220 (FIG. 2 ) with the corresponding partner A cookie 228 and AME cookie 208 in the partner cookie map 236.

In the illustrated example of FIG. 4 , to send computer executable instructions to the client device(s) 108 to monitor user logins via login webpages (e.g., the login webpage 204 of FIG. 2 ), the apparatus 400 is provided with the instructions interface 418. For example, the apparatus 400 may use the instructions interface 418 to send computer executable instructions (e.g., Java, java script, or any other computer language or script) to the client device 108 that are executed by the web browser 302 (FIG. 3 ) to implement the cookie reporter 202 (FIG. 2 ). In some examples, the instructions interface 418 sends the computer executable instructions to the client device 108 in response to receiving a request from the web browser 302 for a login webpage (e.g., the login webpage 204) of an Internet-based service provided by the entity (e.g., one of the database proprietor partners 104 a and 104 b) that implements the apparatus 400. In this manner, the client device 108 can execute the computer executable instructions to monitor login events at the login webpage.

FIG. 15 is an example apparatus that may be used to implement the impression monitor system 102 of FIGS. 1-3 . In the illustrated example, to detect whether AME cookies (e.g., the AME cookie 208 of FIG. 2 ) have been set (e.g., are stored) in client devices (e.g., any of the client devices 108 of FIGS. 1-3 ), the impression monitor system 102 is provided with a cookie status detector 1502. For example, the cookie status detector 1502 may inspect or analyze messages (e.g., the request 206 of FIG. 2 ) from client devices to determine whether AME cookies are present therein. In the illustrated example, to generate AME cookies (e.g., the AME cookie 208 (FIG. 2 )), the impression monitor system 102 is provided with a cookie generator 1504.

In the illustrated example, to generate login timestamps (e.g., the login timestamp 220 of FIG. 2 ), the impression monitor system 102 is provided with a timestamp generator 1506. For example, the timestamp generator 1506 may be implemented using a real-time clock (RTC) or any other timing or clock device or interface to track time and generate timestamps. In the illustrated example, to generate messages (e.g., the response 216 of FIG. 2 ), the impression monitor system 102 is provided with a message generator 1508. In the illustrated example, to log impressions, the impression monitor system 102 is provided with an impression logger 1510. For example, the impression logger 1510 may log impressions in the AME impression store 114 as shown in FIG. 6 .

In the illustrated example, to receive messages and/or information from client devices 108 and send messages and/or information to client devices 108 and/or to partner database proprietors 104 a and 104 b, the impression monitor system 102 is provided with a communication interface 1512. For example, the communication interface 1512 may receive messages such as the tag requests 112 (FIG. 1 ) and the request 206 (FIG. 2 ) from client devices 108. Additionally, the communication interface 1512 may send messages such as the response 216 (FIG. 2 ) to the client devices 108 and send logged impressions (e.g., impressions logged in the AME impression store 114) to partner database proprietors 104 a and 104 b.

FIG. 16 is an example apparatus that may be used to implement a cookie reporter 202 of FIG. 2 . In the illustrated example, to detect log events, the cookie reporter 202 is provided with a login event detector 1602. For example, the login detector 1602 may be configured to monitor login events generated by web browsers (e.g., the web browser 302 of FIG. 3 ) of client devices (e.g., the client devices 108 of FIGS. 1-3 ). In the illustrated example, when a user logs in to the login webpage 204 of FIG. 2 , the login detector 1602 detects a login event.

In the illustrated example, to detect whether AME cookies (e.g., the AME cookie 208 of FIG. 2 ) or partner cookies (e.g., the partner cookie 228 of FIG. 2 ) have been set (e.g., are stored) in client devices (e.g., the client devices 108 of FIGS. 1-3 ), the cookie reporter 202 is provided with a cookie status detector 1604. For example, the cookie status detector 1602 may inspect or analyze cookie files or cookie entries in client devices to determine whether AME cookies (e.g., the AME cookie 208 of FIG. 2 ) or partner cookies (e.g., the partner cookie 228 of FIG. 2 ) have been previously set. In the illustrated example, the cookie status detector 1604 may also determine whether cookies have expired. In the illustrated example, when a cookie expires, it is treated as invalid or as if it no longer exists in a client device and must be set again by a corresponding server domain.

In the illustrated example, to retrieve cookies from storage locations in client devices (e.g., the client devices 108 of FIGS. 1-3 ), the cookie reporter 202 is provided with a cookie interface 1606. For example, the cookie interface 1606 may retrieve AME cookies (e.g., the AME cookie 208 of FIG. 2 ) or partner cookies (e.g., the partner cookie 228 of FIG. 2 ) from their respective storage locations in client devices. In addition, the cookie interface 1606 may also store cookies set by and received from the impression monitor system 102 and/or any partner database proprietor in the client devices.

In the illustrated example, to generate messages (e.g., the tag requests 112 of FIGS. 1 and 3 , the log reporting messages 118 of FIGS. 1 and 2 , and the request 206 of FIG. 2 ), the cookie reporter 202 is provided with a message generator 1608. In the illustrated example, to send messages and/or information to the impression monitor system 102 and/or to partner database proprietors (e.g., the partner database proprietors 104 a and 104 b of FIGS. 1 and 2 ) and/or to receive messages and/or information from the impression monitor system 102, the cookie reporter 202 is provided with a communication interface 1610. For example, the communication interface 1610 may send the tag requests 112 (FIGS. 1 and 3 ) and the request 206 of FIG. 2 to the impression monitor system 102, receive the response 216 (FIG. 2 ) from the impression monitor system 102, and send the login reporting messages 118 (FIGS. 1 and 2 ) to the partner database proprietors 104 a and 104 b.

While example manners of implementing the apparatus 102 and 202 have been illustrated in FIGS. 15 and 16 , one or more of the elements, processes and/or devices illustrated in FIGS. 15 and 16 may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the cookie status detector 1502, the cookie generator 1504, the timestamp generator 1506, the message generator 1508, the impression logger 1510, the communication interface 1512 and/or, more generally, the example apparatus 102 of FIG. 15 may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. In addition, the login event detector 1602, the cookie status detector 1604, the cookie interface 1606, the message generator 1608, the communication interface 1610 and/or, more generally, the example apparatus 202 of FIG. 16 may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the cookie status detector 1502, the cookie generator 1504, the timestamp generator 1506, the message generator 1508, the impression logger 1510, the communication interface 1512 and/or, more generally, the example apparatus 102 and/or any of the login event detector 1602, the cookie status detector 1604, the cookie interface 1606, the message generator 1608, the communication interface 1610 and/or, more generally, the example apparatus 202 could be implemented by one or more circuit(s), programmable processor(s), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)), etc. When any of the apparatus or system claims of this patent are read to cover a purely software and/or firmware implementation, at least one of the cookie status detector 1502, the cookie generator 1504, the timestamp generator 1506, the message generator 1508, the impression logger 1510, the communication interface 1512, the login event detector 1602, the cookie status detector 1604, the cookie interface 1606, the message generator 1608, and/or the communication interface 1610 are hereby expressly defined to include a tangible computer readable medium such as a memory, DVD, CD, BluRay disk, etc. storing the software and/or firmware. Further still, the example apparatus 102 and 202 may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in FIGS. 15 and 16 , and/or may include more than one of any or all of the illustrated elements, processes and devices.

Turning to FIG. 8 , an example impressions totalization data structure 800, which may be generated by the report generator 412 of FIG. 4 , stores impression totalizations based on the impressions logged by the impression monitor system 102 (FIGS. 1-3 ). As shown in FIG. 8 , the impressions totalization structure 800 shows quantities of impressions logged for the client devices 108 (FIGS. 1-3 ). In the illustrated example, the impressions totalization structure 800 is generated by the report generator 412 for an advertisement campaign (e.g., one or more of the advertisements 110 of FIG. 1 ) to determine frequencies of impressions per day for each monitored user.

To track frequencies of impressions per unique user per day, the impressions totalization structure 800 is provided with a frequency column 802. A frequency of 1 indicates one exposure per day of an ad campaign to a unique user, while a frequency of 4 indicates four exposures per day of the same ad campaign to a unique user. To track the quantity of unique users to which impressions are attributable, the impressions totalization structure 800 is provided with a UUIDs column 804. A value of 100,000 in the UUIDs column 804 is indicative of 100,000 unique users. Thus, the first entry of the impressions totalization structure 800 indicates that 100,000 unique users (i.e., UUIDs=100,000) were exposed once (i.e., frequency=1) in a single day to a particular ad campaign.

To track impressions based on exposure frequency and UUIDs, the impressions totalization structure 800 is provided with an impressions column 806. Each impression count stored in the impressions column 806 is determined by multiplying a corresponding frequency value stored in the frequency column 802 with a corresponding UUID value stored in the UUID column 804. For example, in the second entry of the impressions totalization structure 800, the frequency value of two is multiplied by 200,000 unique users to determine that 400,000 impressions are attributable to a particular ad campaign.

Turning to FIG. 9 , an ad campaign-level age/gender and impression composition data structure 900 is shown, which, in the illustrated example, may be generated by the report generator 412 of FIG. 4 . The impression data in the ad campaign-level age/gender and impression composition structure 900 of FIG. 9 corresponds to impressions attributable to registered user of a particular partner database (DB) proprietor (e.g., the partner A database proprietor 104 a of FIGS. 1 and 2 or the partner B database proprietor 104 b of FIG. 1 ). Similar tables can be generated for content and/or other media. Additionally or alternatively, other media in addition to advertisements may be added to the data structure 900.

The ad campaign-level age/gender and impression composition structure 900 is provided with an age/gender column 902, an impressions column 904, a frequency column 906, and an impression composition column 908. The age/gender column 902 of the illustrated example indicates different age/gender demographic groups. The impressions column 904 of the illustrated example stores values indicative of the total impressions for a particular ad campaign for corresponding age/gender demographic groups. The frequency column 906 of the illustrated example stores values indicative of the frequency of exposure per user for the ad campaign that contributed to the impressions in the impressions column 904. The impressions composition column 908 of the illustrated example stores the percentage of impressions for each of the age/gender demographic groups.

In some examples, the demographics analyzer 406 and the demographics modifier 408 of FIG. 4 perform demographic accuracy analyses and adjustment processes on demographic information before tabulating final results of impression-based demographic information in the campaign-level age/gender and impression composition table 900. This can be done to address a problem facing online audience measurement processes in that the manner in which registered users represent themselves to online database proprietors (e.g., the partners 104 a and 104 b) is not necessarily veridical (e.g., truthful and/or accurate). In some instances, example approaches to online measurements that leverage account registrations at such online database proprietors to determine demographic attributes of an audience may lead to inaccurate demographic-exposure results if they rely on self-reporting of personal/demographic information by the registered users during account registration at the database proprietor site. There may be numerous reasons for why users report erroneous or inaccurate demographic information when registering for database proprietor services. The self-reporting registration processes used to collect the demographic information at the database proprietor sites (e.g., social media sites) does not facilitate determining the veracity of the self-reported demographic information. In some examples, to analyze and/or adjust inaccurate demographic information, the demographics analyzer 406 and/or the demographics modifier 408 may use example methods, systems, apparatus, and/or articles of manufacture disclosed in U.S. patent application Ser. No. 13/209,292, filed on Aug. 12, 2011, and titled “Methods and Apparatus to Analyze and Adjust Demographic Information,” which is hereby incorporated herein by reference in its entirety.

Although the example ad campaign-level age/gender and impression composition structure 900 shows impression statistics in connection with only age/gender demographic information, the report generator 412 of FIG. 4 may generate the same or other data structures to additionally or alternatively include other types of demographic information. In this manner, the report generator 412 can generate the impression reports 106 a (FIGS. 1 and 4 ) to reflect impressions based on different types of demographics and/or different types of media.

FIGS. 10-13 are flow diagrams representative of machine readable instructions that can be executed to implement the apparatus and systems of FIGS. 1, 2, 3 , and/or 4. The example processes of FIGS. 10-13 may be implemented using machine readable instructions that, when executed, cause a device (e.g., a programmable controller or other programmable machine or integrated circuit) to perform the operations shown in FIGS. 10-13 . In this example, the machine readable instructions comprise a program for execution by a processor such as the processor 1412 shown in the example computer 1410 discussed below in connection with FIG. 14 . The program may be embodied in software stored on a tangible computer readable medium such as a CD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), a BluRay disk, a flash memory, a read-only memory (ROM), a random-access memory (RAM), or a memory associated with the processor 1412, but the entire program and/or parts thereof could alternatively be executed by a device other than the processor 1412 and/or embodied in firmware or dedicated hardware.

As used herein, the term tangible computer readable medium is expressly defined to include any type of computer readable storage and to exclude propagating signals. Additionally or alternatively, the example processes of FIGS. 10-13 may be implemented using coded instructions (e.g., computer readable instructions) stored on a non-transitory computer readable medium such as a flash memory, a read-only memory (ROM), a random-access memory (RAM), a cache, or any other storage media in which information is stored for any duration (e.g., for extended time periods, permanently, brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable medium and to exclude propagating signals. As used herein, when the phrase “at least” is used as the transition term in a preamble of a claim, it is open-ended in the same manner as the term “comprising” is open ended. Thus, a claim using “at least” as the transition term in its preamble may include elements in addition to those expressly recited in the claim.

Alternatively, the example processes of FIGS. 10-13 may be implemented using any combination(s) of application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), field programmable logic device(s) (FPLD(s)), discrete logic, hardware, firmware, etc. Also, the example processes of FIGS. 10-13 may be implemented as any combination(s) of any of the foregoing techniques, for example, any combination of firmware, software, discrete logic and/or hardware.

Although the example processes of FIGS. 10-13 are described with reference to the flow diagrams of FIGS. 10-13 , other methods of implementing the apparatus and systems of FIGS. 1, 2, 3 , and/or 4 may be employed. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, sub-divided, or combined. Additionally, one or both of the example processes of FIGS. 10-13 may be performed sequentially and/or in parallel by, for example, separate processing threads, processors, devices, discrete logic, circuits, etc.

Turning in detail to FIG. 10 , the depicted example processes may be used to report login events and user cookies (e.g., the AME cookie 208 and the partner A cookie 228 of FIGS. 2 and 3 ) to database proprietors (e.g., the partner A database proprietor 104 a of FIGS. 1 and 2 ). In the illustrated example, the flow diagram shows a client device process 1002 and an impression monitor system process 1004. In the illustrated example, the client device process 1002 may be performed by the cookie reporter 202 of FIGS. 2 and 16 , and the impression monitor system process 1004 may be implemented by the impression monitor system 102 of FIGS. 1-3 and 15 . The example processes of FIG. 10 are described in connection with FIG. 2 as interactions between the client device 108, the impression monitor system 102, and the partner A database proprietor 104 a. However, processes similar or identical to the example processes of FIG. 10 may be performed at any time or at the same time between other client devices, the impression monitor system 102 and/or other database proprietors to accomplish the same type of user login reporting events when users login to login pages (e.g., the login page 204 of FIG. 2 ) of respective database proprietors (e.g., the database proprietors 104 a and 104 b of FIGS. 1 and 2 ).

Initially, as part of the client device process 1002, the login event detector 1602 (FIG. 16 ) detects a login event (block 1006). The login event may be, for example, a user of the client device 108 logging into the login page 204 of FIG. 2 . The message generator 1608 (FIG. 16 ) generates the request 206 (FIG. 2 ) to indicate the login event (block 1008). The cookie status detector 1604 (FIG. 16 ) determines whether an AME cookie (e.g. the AME cookie 208 of FIG. 2 ) is already set in the client device 108 (block 1010). If the AME cookie 208 is already set, the cookie interface 1606 (FIG. 16 ) and/or the message generator 1608 store(s) the AME cookie 208 (e.g., a name-value pair identifying a user) in the request 206 (block 1012). After storing the AME cookie 208 in the request 206 (block 1012) or if the AME cookie 208 is not already set in the client device (block 1010), the communication interface 1610 (FIG. 16 ) sends the request 206 to the impression monitor system 102 (block 1014).

As shown in the example impression monitor system process 1004, the communication interface 1512 (FIG. 15 ) receives the request 206 (block 1016), and the cookie status detector 1502 (FIG. 15 ) determines whether the AME cookie 208 is already set in the client device 108 (block 1018). For example, the cookie status detector 1502 can determine whether the AME cookie 208 is already set based on whether the request 206 contains the AME cookie 208. If the cookie status detector 1502 determines that the AME cookie 208 is not already set (block 1018), the cookie generator 1504 (FIG. creates the AME cookie 208 (block 1020). For example, the cookie generator 1504 can generate the AME cookie 208 by generating a UUID for the client device 108. The message generator 1508 (FIG. 15 ) stores the AME cookie 208 in the response 216 (FIG. 2 ) (block 1022).

After storing the AME cookie 208 in the response 216 (block 1022) or if the cookie status detector 1502 determines at block 1018 that the AME cookie 208 is already set in the client device 108, the timestamp generator 1506 generates a login timestamp (e.g., the login timestamp 220 of FIG. 2 ) (block 1024) to indicate a login time for the detected login event. The message generator 1508 stores the login timestamp 220 in the response 216 (block 1026), and the communication interface 1512 sends the response 216 to the client device 108 (block 1028).

Returning to the client device process 1002, the communication interface 1610 (FIG. 16 ) receives the response 216 (block 1030), and the message generator 1608 (FIG. 16 ) generates the login reporting message 118 (FIGS. 1 and 2 ) (block 1032). If present, the cookie interface 1606 (FIG. 16 ) and/or the message generator 1608 store(s) a partner cookie corresponding to the login event detected at block 1006 (e.g., the partner A cookie 228) in the login reporting message 118 (block 1034). If a corresponding partner cookie is not present in the client device 108, a partner cookie is not stored in the login reporting message 118 to indicate to the corresponding partner that it should create a partner cookie for the client device 108. In addition, the cookie interface 1606 and/or the message generator 1608 store(s) the AME cookie 208 as a data parameter (e.g., in the payload 232) in the login reporting message 118 (block 1036). The message generator 1608 also stores the login timestamp 220 in the login reporting message 118 (e.g., in the payload 232) (block 1038). The communication interface 1610 sends the login reporting message 118 to a corresponding partner database proprietor (e.g., the partner A database proprietor 104 a) (block 1040). In this manner, the cookie reporter 202 enables the partner A database proprietor 104 a to map the partner A cookie 228 to the AME cookie 208 and the login timestamp 220 in the partner cookie map 236 of FIGS. 2 and 5 . The example process of FIG. 10 then ends.

Turning now to FIG. 11 , the depicted flow diagram is representative of an example process that may be performed by a partner database proprietor (e.g., the partner database proprietors 104 a and/or 104 b of FIGS. 1 and 2 ) to map AME cookie identifiers (e.g., a UUID of the AME cookie 208 of FIG. 2 ) with user identifiers (e.g., a UUID of the partner A cookie 228 of FIG. 2 ) of users registered with the partner database proprietor. While for simplicity, FIG. 11 refers to a process receiving a single login message, many such processes may exist and execute in parallel (e.g., parallel threads). The example process of FIG. 11 is described in connection with the illustrated example of FIG. 2 , the apparatus 400 of FIG. 4 , and the partner A database proprietor 104 a. However, processes similar or identical to the example processes of FIG. 11 may be performed at any time or at the same time by other partner database proprietors and/or other apparatus to accomplish the same type of cookie mapping process.

Initially, the partner A database proprietor 104 a receives the login reporting message 118 (FIGS. 1 and 2 ) (block 1102). The data parser 414 (FIG. 4 ) extracts the partner A cookie 228 (block 1104) from the login reporting message 118. In the illustrated example, the data parser 414 extracts the partner A cookie 228 from the cookie field 230 of the login reporting message 118. The data parser 414 extracts the AME cookie 208 (block 1106) from the login reporting message 118. In the illustrated example, the data parser 414 extracts the AME cookie 208 as a data parameter from the payload 232 of the login reporting message 118. In addition, the data parser 414 extracts the login timestamp 220 from the login reporting message 118 (block 1108). The mapper 416 (FIG. 4 ) maps the partner A cookie 228 to the AME cookie 208 (e.g., maps the UUIDs of each cookie to one another) (block 1110) in, for example, the partner cookie map 236 of FIGS. 2 and 5 . In addition, the mapper 416 stores the login timestamp 220 in association with the mapped cookies (block 1112) in the partner cookie map 236. The example process of FIG. 11 then ends.

Now turning to FIG. 12 , the depicted example process may be performed to log impressions. In the illustrated example, the example process of FIG. 12 is described in connection with FIGS. 3 and 15 as being performed by the impression monitor system 102 based on tag requests received from the client device 108. However, processes similar or identical to the example process of FIG. 12 may be performed at any time or at the same time (e.g., multiple threads may be spawned and execute in parallel) by the impression monitor system 102 in connection with other client devices (e.g., any of the client devices 108 of FIG. 1 or any other client devices) to log impressions attributable to those client devices.

Initially, the communication interface 1512 (FIG. 15 ) receives a tag request (e.g., the tag request 112 of FIGS. 1 and 3 ) (block 1202). The impression logger 1510 (FIG. 15 ) logs an impression for an AME UUID indicated by the AME cookie 208 (block 1204). In the illustrated example, the impression logger 1510 logs the impression in the AME impression store 114 of FIGS. 1, 3, and 6 . The impression logger 1510 determines whether it should send the AME impression logs 122 (FIGS. 1 and 4 ) to one or more partner database proprietors (block 1206). For example, the impression logger 1510 may be configured to periodically or aperiodically send the AME impression logs 122 to one or more partner database proprietors (e.g., the partner database proprietors 104 a and 104 b of FIGS. 1 and 2 ) based on one or more of a schedule and/or a threshold of logged impressions.

If the impression logger 1510 determines that it should send the AME impression logs 122 to one or more partner database proprietors (block 1206), the communication interface 1512 sends the AME impression logs 122 to the one or more partner database proprietors (block 1208). In response, the communication interface 1512 receives one or more impression reports (e.g., the impression reports 106 a and 106 b of FIGS. 1 and 4 ) from the one or more partner database proprietors (block 1210). In some examples, the impression monitor system 102 applies weighting factors to impression audience data in impression reports from different database proprietors (e.g., the partner database proprietors 104 a and 104 b). In some examples, the weighting factors are determined for each database proprietor based on, for example, demographic distributions and/or impression distributions in the impression data and/or sample sizes (e.g., the quantity of registered users of a particular database proprietor, the quantity of registered users monitored for the particular database proprietor, and/or the quantity of impressions logged by the AME 103 for registered users of the particular database proprietor).

After receiving the one or more impression reports (block 1210) or if at block 1206 the impression logger 1510 determines that it should not send the AME impression logs 122 to one or more partner database proprietors, the impression monitor system 102 determines whether it should continue to monitor impressions (block 1212). For example, the impression monitor system 102 may be configured to monitor impressions until it is turned off or disabled. If the impression monitor system 102 determines that it should continue to monitor impressions (block 1212), control returns to block 1202. Otherwise, the example process of FIG. 12 ends.

Turning now to FIG. 13 , the depicted example process may be used to generate demographics-based impressions reports (e.g., the impression reports 106 a and 106 b of FIGS. 1 and 4 ). The example process of FIG. 13 is described in connection with FIG. 4 as being implemented by the example apparatus 400 via the partner A database proprietor 104 a. However, processes similar or identical to the example process of FIG. 13 may be performed at any time or at the same time by any other partner database proprietor(s) to generate impression reports based on registered users of those partner database proprietor(s).

Initially, the apparatus 400 receives the AME impression logs 122 (FIG. 4 ) (block 1302). The cookie matcher 402 (FIG. 4 ) matches AME cookies to partner database proprietor cookies (block 1304). For example, the cookie matcher 402 can use a cookie map of the corresponding database proprietor (e.g., the partner A cookie map 236 (FIG. 4 )) to match UUIDs from AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3 ) indicated in the AME impression logs 122 to UUIDs from partner database proprietor cookies (e.g., the partner A database proprietor cookie 228 of FIGS. 2 and 3 ). The cookie matcher 402 then associates impressions (e.g., impressions noted in the AME impression logs 122) to corresponding partner database proprietor UUIDs (block 1306) based on matches found at block 1304. For example, the cookie matcher 402 may generate the partner-based impressions data structure 700 described above in connection with FIG. 7 .

The demographics associator 404 (FIG. 4 ) associates demographics of registered users of the corresponding database proprietor (e.g., the partner A database proprietor 104 a) to the impressions (block 1308). For example, the demographics associator 404 may retrieve demographics information from the user accounts database 238 (FIGS. 2 and 4 ) for partner user IDs noted in the partner user ID column 712 of the partner-based impressions data structure 700.

The user ID modifier 410 removes user IDs from the demographics-based impressions data structure 700 (block 1310). For example, the user ID modifier 410 can remove UUIDs from the AME user ID column 702 corresponding to AME cookies (e.g., the AME cookie 208 of FIGS. 2 and 3 ) and the partner user ID column 712 corresponding to partner cookies (e.g., the partner A cookie 228 of FIGS. 2 and 3 ). In other examples, the report generator 412 can copy selected portions from the demographics-based impressions data structure 700 and store the selected portions in a report without copying over the user IDs. In this manner, the apparatus 400 can obfuscate identities of registered users to protect their privacy when the demographics-based impressions are shared with others (e.g., an audience measurement entity).

The demographics analyzer 406 (FIG. 4 ) analyzes the demographics information (block 1312) that was associated with the impressions at block 1308. The demographics analyzer 406 determines whether any demographics information needs to be modified (block 1314). If any of the demographics information needs to be modified (e.g., demographics information needs to be changed or added due to being incomplete and/or inaccurate), the demographics modifier 408 (FIG. 4 ) modifies select demographics data needing modification (block 1316). In the illustrated example, the demographics analyzer 406 and/or the demographics modifier 408 may perform the operations of blocks 1312, 1314, and 1316 to analyze and/or modify demographics information using, for example, one or more example techniques disclosed in U.S. patent application Ser. No. 13/209,292, filed on Aug. 12, 2011, and titled “Methods and Apparatus to Analyze and Adjust Demographic Information,” which is hereby incorporated herein by reference in its entirety.

After modifying demographics information at block 1316 or if at block 1314 the demographics analyzer 406 determines that none of the demographics information requires modification, the report generator 412 generates one or more impression reports (e.g., the impression reports 106 a of FIGS. 1 and 4 ) (block 1318). For example, the report generator 412 may generate one or more of the impression reports 106 a using one or more example techniques described above in connection with FIGS. 8 and 9 and/or using any other suitable technique(s). The apparatus 400 then sends the one or more impression reports 106 a to the impression monitor system 102 (block 1320). In the illustrated example, the impression reports 106 a are indicative of demographic segments, populations, or groups associated with different AME cookies 208 (and corresponding partner A cookies 228) and that were exposed to content (e.g., advertisements and/or other media) identified by campaign IDs and/or other the media content IDs. The example process of FIG. 13 then ends.

FIG. 14 is a block diagram of an example processor system 1410 that may be used to implement the example apparatus, methods, and systems disclosed herein. As shown in FIG. 14 , the processor system 1410 includes a processor 1412 that is coupled to an interconnection bus 1414. The processor 1412 may be any suitable processor, processing unit, or microprocessor. Although not shown in FIG. 14 , the system 1410 may be a multi-processor system and, thus, may include one or more additional processors that are identical or similar to the processor 1412 and that are communicatively coupled to the interconnection bus 1414.

The processor 1412 of FIG. 14 is coupled to a chipset 1418, which includes a memory controller 1420 and an input/output (I/O) controller 1422. A chipset provides I/O and memory management functions as well as a plurality of general purpose and/or special purpose registers, timers, etc. that are accessible or used by one or more processors coupled to the chipset 1418. The memory controller 1420 performs functions that enable the processor 1412 (or processors if there are multiple processors) to access a system memory 1424, a mass storage memory 1425, and/or an optical media 1427.

In general, the system memory 1424 may include any desired type of volatile and/or non-volatile memory such as, for example, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, read-only memory (ROM), etc. The mass storage memory 1425 may include any desired type of mass storage device including hard disk drives, optical drives, tape storage devices, etc. The optical media 1427 may include any desired type of optical media such as a digital versatile disc (DVD), a compact disc (CD), or a blu-ray optical disc. The instructions of any of FIGS. 10-13 may be stored on any of the tangible media represented by the system memory 1424, the mass storage device 1425, the optical media 1427, and/or any other media.

The I/O controller 1422 performs functions that enable the processor 1412 to communicate with peripheral input/output (I/O) devices 1426 and 1428 and a network interface 1430 via an I/O bus 1432. The I/O devices 1426 and 1428 may be any desired type of I/O device such as, for example, a keyboard, a video display or monitor, a mouse, etc. The network interface 1430 may be, for example, an Ethernet device, an asynchronous transfer mode (ATM) device, an 802.11 device, a digital subscriber line (DSL) modem, a cable modem, a cellular modem, etc. that enables the processor system 1410 to communicate with another processor system.

While the memory controller 1420 and the I/O controller 1422 are depicted in FIG. 14 as separate functional blocks within the chipset 1418, the functions performed by these blocks may be integrated within a single semiconductor circuit or may be implemented using two or more separate integrated circuits.

Although the above discloses example methods, apparatus, systems, and articles of manufacture including, among other components, firmware and/or software executed on hardware, it should be noted that such methods, apparatus, systems, and articles of manufacture are merely illustrative and should not be considered as limiting. Accordingly, while the above describes example methods, apparatus, systems, and articles of manufacture, the examples provided are not the only ways to implement such methods, apparatus, systems, and articles of manufacture.

Although certain example methods, apparatus and articles of manufacture have been described herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent. 

1. (canceled)
 2. A method comprising: obtaining, at a server, a response to a hypertext transfer protocol (HTTP) request as a network communication from a client device via a network; logging, at the server, a website impression of a website by the client device based on the response to the HTTP request obtained via the website, the website impression including a site identifier corresponding to the website and a timestamp; determining a demographic weighting factor for impressions of the website based on demographic distributions of registered users of a database proprietor, the impressions of the website including the website impression; weighting the impressions of the website based on the demographic weighting factor for the impressions of the website to obtain weighted impression data; and generating a report including the weighted impression data.
 3. The method of claim 2, wherein the website impression of the website by the client device is representative of a website visit to the website, the website visit associated with the HTTP request.
 4. The method of claim 2, where the website impression further includes a user identifier different from a type of identifier used to identify the registered users of the database proprietor.
 5. The method of claim 4, wherein the user identifier is a cookie identifier associated with the server.
 6. The method of claim 2, wherein determining the demographic weighting factor for the impressions of the website comprises associating the demographic distributions of the registered users of the database proprietor with a number of website visits to the website by the registered users of the database proprietor.
 7. The method of claim 2, wherein determining the demographic weighting factor for the impressions of the website comprises retrieving demographic information for each user account of the registered users of the database proprietor as the demographic distributions of the registered users of the database proprietor.
 8. The method of claim 2, wherein determining the demographic weighting factor for the impressions of the website comprises identifying a quantity of the registered users of the database proprietor.
 9. The method of claim 2, wherein the client device is at least one of a computer or a phone.
 10. The method of claim 2, where the database proprietor is at least one of a social network proprietor, a search engine proprietor, an online retail proprietor, or any proprietor having a database and connected to the server.
 11. A non-transitory computer-readable storage medium, having stored thereon instructions that, upon execution by a processor, cause performance of a set of operations comprising: logging a website impression of a website by a client device based on a hypertext transfer protocol (HTTP) communication, the website impression including a site identifier corresponding to the website; determining a demographic weighting factor based on demographic distributions of registered users of a database proprietor; weighting impressions of the website including the website impression based on the demographic weighting factor; and generating a report including the weighted impressions of the website.
 12. The non-transitory computer-readable storage medium of claim 11, wherein the HTTP communication is an HTTP response obtained from the website.
 13. The non-transitory computer-readable storage medium of claim 11, wherein the website impression of the website by the client device is representative of a website visit to the website, the website visit associated with the HTTP communication.
 14. The non-transitory computer-readable storage medium of claim 11, the set of operations further comprising: associating the demographic distributions of the registered users of the database proprietor with a number of website visits to the website.
 15. The non-transitory computer-readable storage medium of claim 11, the set of operations further comprising: retrieving demographic information regarding the registered users of the database provider as the demographic distributions of the registered users of the database provider.
 16. A computing system comprising: a processor; and a non-transitory computer-readable storage medium, having stored thereon instructions that, upon execution by the processor, cause performance of a set of operations comprising: logging a website impression of a website based on a hypertext transfer protocol (HTTP) communication, the website impression including a site identifier corresponding to the website; determining a demographic weighting factor based on demographic distributions of registered users associated with a database proprietor; weighting impressions of the website including the website impression based on the demographic weighting factor; and generating a report including the weighted impressions of the website.
 17. The computing system of claim 16, the set of operations further comprising: obtaining, via the Internet, the demographic distributions of the registered users associated with the database proprietor.
 18. The computing system of claim 16, wherein the computing system further comprises: a client device interacting with the website, and wherein the set of operations further comprises: obtaining the HTTP communication, as an HTTP response, from the website based on a website visit by the client device.
 19. The computing system of claim 18, wherein the client device is at least one of a computer or phone.
 20. The computing system of claim 16, the set of operations further comprising: associating the demographic distributions of the registered users of the database proprietor with a number of website visits to the website.
 21. The computing system of claim 16, the set of operations further comprising: retrieving the demographic distributions of the registered users of the database provider. 